Overview
Hailo provides hardware and software solutions that enable high-performance deep learning inference on resource-constrained edge devices. Its portfolio includes the Hailo-8 family (high-efficiency multi-model, multi-stream inference) and the Hailo-10H (generative-AI-capable accelerator with a DDR interface for scaling large models). The platform combines a structure-driven dataflow architecture with a Dataflow Compiler, the HailoRT runtime, and model zoos (Vision and GenAI) to simplify model porting, optimization, and deployment across x86 and ARM hosts and Linux/Windows/Android targets. Hailo targets commercial, industrial, automotive, and personal-compute edge markets, emphasizing low latency, low power consumption, industrial operating ranges, and a mature software ecosystem (framework support for TensorFlow, PyTorch, ONNX, etc.). Notes & gaps discovered during collection: No public pricing page or self-serve pricing tiers were found on hailo.ai (pricing appears to be provided via sales/distributors). The company's launch/founding year was not found on the visited pages. A single canonical public documentation domain or distinct “docs” URL was not located during the scrape; documentation links and references are present on product and software pages. A clear single support/contact page was not identified on the visited pages.
Key Features
Hailo-10H Generative AI Acceleration
Enables LLMs, VLMs, and diffusion models on edge devices with up to 40 TOPS INT4 (20 TOPS INT8) performance and a DDR interface for scalable memory bandwidth.
Hailo-8 Edge AI Accelerator
High-efficiency neural processor for real-time on-device inference: up to ~26 TOPS at ~2.5W typical, supporting multi-stream and multi-model inference without external memory.
Dataflow Compiler
Offline model translation and optimization that maps networks to Hailo’s structure-driven dataflow architecture for efficient execution and portability.
HailoRT Runtime
Lightweight, production-grade runtime with C/C++ and Python APIs for deploying and running models on host systems or dedicated Hailo hardware.
Model Zoos & Model Explorer
Pre-built Vision and GenAI model zoos with reference binaries, hardware benchmarks, and tools to browse and evaluate models on real hardware.
Multi-Platform & Framework Support
Interoperability with TensorFlow, TensorFlow Lite, PyTorch, Keras, ONNX and runtimes; runs on x86/ARM hosts and Linux/Windows/Android targets.
Who Can Use This Tool?
- Developers:Port, optimize, and deploy deep learning models to edge devices using Hailo toolchain.
- OEMs & Integrators:Integrate Hailo modules/SoCs into products for low-latency, energy-efficient edge AI inference.
- Automotive & Industrial:Deploy robust on-device perception and generative AI solutions in constrained industrial or automotive environments.
Pricing Plans
Pricing information is not available yet.
Pros & Cons
✓ Pros
- ✓High performance per watt (Hailo-8: up to ~26 TOPS @ ~2.5W; Hailo-10H: up to 40 TOPS INT4 / 20 TOPS INT8 with ~2.5W typical cited)
- ✓Designed for multi-stream and multi-model inference (Hailo-8) and scalable DDR interface for larger models (Hailo-10H)
- ✓Mature software stack including Dataflow Compiler, HailoRT runtime, model zoos, profilers, emulator, and examples
- ✓Broad framework support (TensorFlow, TensorFlow Lite, PyTorch, Keras, ONNX) and host-OS support (Linux, Windows, Android)
- ✓Industrial and automotive operating ranges and mentions of applicable compliance where relevant
✗ Cons
- ✗No public pricing or clear self-serve purchase tiers; product inquiries appear to go through sales/distributors
- ✗Integration requires use of a specialized dataflow compiler and toolchain (learning curve for porting and optimization)
- ✗Not targeted at casual/consumer plug-and-play usage (primarily focused on OEMs, system integrators, and product teams)
Compare with Alternatives
| Feature | Hailo | EnCharge AI | Rebellions.ai |
|---|---|---|---|
| Pricing | N/A | N/A | N/A |
| Rating | 8.2/10 | 8.1/10 | 8.4/10 |
| Inference Efficiency | Low-power high-performance inference | Analog in-memory compute for very high efficiency | High-throughput energy-efficient inference for hyperscale |
| On-Device GenAI | Yes | Partial | No |
| Compiler & Runtime | Yes | Partial | Yes |
| Memory Architecture | No | Yes | Yes |
| Form-factor Support | Yes | Yes | Yes |
| Model Ecosystem | Yes | Partial | Yes |
| Industrial Readiness | Yes | Partial | Yes |
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